Abstract

Background

Combining the results of studies using highly parallelized measurements of gene expression
such as microarrays and RNAseq offer unique challenges in meta analysis. Motivated
by a need for a deeper understanding of organ transplant rejection, we combine the
data from five separate studies to compare acute rejection versus stability after
solid organ transplantation, and use this data to examine approaches to multiplex
meta analysis.

Results

We demonstrate that a commonly used parametric effect size estimate approach and a
commonly used non-parametric method give very different results in prioritizing genes.
The parametric method providing a meta effect estimate was superior at ranking genes
based on our gold-standard of identifying immune response genes in the transplant
rejection datasets.

Conclusion

Different methods of multiplex analysis can give substantially different results.
The method which is best for any given application will likely depend on the particular
domain, and it remains for future work to see if any one method is consistently better
at identifying important biological signal across gene expression experiments.